Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Rough sets and fuzzy sets—some remarks on interrelations
Fuzzy Sets and Systems
Logic programs with classical negation
Logic programming
Answer sets and constructive logic, II: extended logic programs and related nonmonotonic formalisms
Proceedings of the second international workshop on Logic programming and non-monotonic reasoning
Variable precision rough set model
Journal of Computer and System Sciences
Tabled evaluation with delaying for general logic programs
Journal of the ACM (JACM)
Prolog: the standard: reference manual
Prolog: the standard: reference manual
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
An abstract machine for tabled execution of fixed-order stratified logic programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
A survey of paraconsistent semantics for logic programs
Handbook of defeasible reasoning and uncertainty management systems
Complexity and expressive power of logic programming
ACM Computing Surveys (CSUR)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Logic, Programming, and PROLOG
Logic, Programming, and PROLOG
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Tabling for non-monotonic programming
Annals of Mathematics and Artificial Intelligence
Key Constraints and Monotonic Aggregates in Deductive Databases
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Classification of Gene Expression Data in an Ontology
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Discovery of Decision Rules by Matching New Objects Against Data Tables
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
A Rough Set Approach to Inductive Logic Programming
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
VPRSM Approach to WEB Searching
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A Logic Programming Framework for Rough Sets
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
A Rough Set Framework for Learning in a Directed Acyclic Graph
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Handbook of data mining and knowledge discovery
Knowledge Representation and Reasoning
Knowledge Representation and Reasoning
From rough sets to rough knowledge bases
Fundamenta Informaticae
Query answering in rough knowledge bases
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Modeling and Reasoning with Paraconsistent Rough Sets
Fundamenta Informaticae
A four-valued logic for rough set-like approximate reasoning
Transactions on rough sets VI
Four-valued extension of rough sets
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Modeling and Reasoning with Paraconsistent Rough Sets
Fundamenta Informaticae
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Rough sets framework has two appealing aspects. First, it is a mathematical approach to deal with vague concepts. Second, rough set techniques can be used in data analysis to find patterns hidden in the data. The number of applications of rough sets to practical problems in different fields demonstrates the increasing interest in this framework and its applicability. This thesis proposes a language that caters for implicit definitions of rough sets obtained by combining different regions of other rough sets. In this way, concept approximations can be derived by taking into account domain knowledge. A declarative semantics for the language is also discussed. It is then shown that programs in the proposed language can be compiled to extended logic programs under the paraconsistent stable model semantics. The equivalence between the declarative semantics of the language and the declarative semantics of the compiled programs is proved. This transformation provides the computational basis for implementing our ideas. A query language for retrieving information about the concepts represented through the defined rough sets is also discussed. Several motivating applications are described. Finally, an extension of the proposed language with numerical measures is presented. This extension is motivated by the fact that numerical measures are an important aspect in data mining applications.